Universiti Teknologi Malaysia Institutional Repository

Lack of training data in sentiment classification: current solution

Hajmohammadi, Mohammad Sadegh and Ibrahim, Roliana (2012) Lack of training data in sentiment classification: current solution. International Journal of Research in Computer and Communication Technology, 1 (4). pp. 133-138. ISSN 2278-5841

Full text not available from this repository.

Official URL: http://www.ijrcct.org/index.php/ojs/article/view/5...

Abstract

In recent years, sentiment classification has attracted much attention from natural language processing researchers. Most of researchers in this field consider sentiment classification as a supervised classification problem and train a classifier from a large number of labelled documents. . Unfortunately, in some language other than English, a reliable and sufficient labelled data is not always available and manually labelling a reliable and rich training data is very time-consuming. Until now, researchers have developed several techniques to the solution of the problem. This paper try to cover some techniques and approaches that be used in this area.

Item Type:Article
Uncontrolled Keywords:sentiment classification, labelled data, unlabeled data
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:31074
Deposited By: Yanti Mohd Shah
Deposited On:03 Mar 2014 04:20
Last Modified:30 Nov 2018 07:09

Repository Staff Only: item control page